The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 8; s. 2382
Hlavní autoři: Gao, Yangde, Ahmad, Zahoor, Kim, Jong-Myon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 09.04.2024
MDPI
Témata:
Bearings
Communication
convolutional LSTM autoencoder
Deep learning
Eigenvalues
health index analysis
Kurtosis
Machinery
Neural networks
remaining useful life
Signal processing
Support vector machines
Useful life
health index analysis
convolutional LSTM autoencoder
remaining useful life
ISSN:1424-8220, 1424-8220
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Shrnutí:The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals. Second, based on the peak value properties, a novel health index (HI) is designed to analyze the health conditions for the denoising signal, showing a high sensitivity for the degradation of bearings. Finally, for better prognostics and health management of the rotating machinery, based on convolutional layers and LSTM, an autoencoder can achieve a transform convolutional LSTM network to develop a convolutional LSTM autoencoder (ALSTM) model that can be applied to forecast the health trend for rotating machinery. Compared with the SVM, CNN, LSTM, GRU, and DTGRU methods, our experiments demonstrate that the proposed approach has the greatest performance for the prediction of the remaining useful life of rotating machinery.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24082382